# -*- coding: utf-8 -*-

import time
import os
from PyQt5.QtCore import QThread, pyqtSignal
from PyQt5.QtGui import QPixmap
from train import TRAIN_PIC_SIZE,MODEL_DIR_NAME
from Preprocess import IMG_Path,LB_Path,CSV_Path,mkDir,YOLO_PATH

import argparse
import os
import sys
from pathlib import Path

import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn

from yolov5.models.experimental import attempt_load
from yolov5.utils.datasets import LoadImages, LoadStreams
from yolov5.utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
    strip_optimizer, xyxy2xywh
from yolov5.utils.plots import Annotator, colors
from yolov5.utils.torch_utils import load_classifier, select_device, time_sync

DETECT_SAVE_DIR='res'
ROOT = YOLO_PATH

class DetectThread(QThread):
    # 使用信号和UI主线程通讯，参数是发送信号时附带参数的数据类型，可以是str、int、list等
    finishSignal = pyqtSignal(str)
    detect_finish_sign=pyqtSignal(bool)

    # 带参数示例
    def __init__(self, weights='', project=DETECT_SAVE_DIR,save_conf=0.5,save_dir_name='cattle_detect_res',imgsz=TRAIN_PIC_SIZE,parent=None):
        super(DetectThread, self).__init__(parent)

        self.weights = ''
        self.project = os.path.join(os.getcwd(),project)
        self.imgsz=imgsz
        self.device = select_device('')
        self.save_dir=save_dir_name
        self.save_txt=False

        if(weights!=''):
            self.my_lodelmodel(self.weights)
        self.detected_flag=False #更换图片或者模型时置为False；detect完成置True
        self.labels=[]
        self.im0= cv2.cvtColor(np.zeros((3,3),dtype=np.uint8), cv2.COLOR_GRAY2BGR)
        self.crops=[]
        self.save_conf=save_conf

    def run(self):
        '''
        重写
        '''
        print('Begin to detect used model {}'.format(self.weights))
        self.finishSignal.emit(self.weights)
        return

    def my_loadmodel(self,weights):
        parser = argparse.ArgumentParser()
        parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
        parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt',
                            help='model.pt path(s)')
        opt = parser.parse_args()
        device = select_device(opt.device)

        # device ='cuda:0'
        print("device", device)

        self.weights=weights
        # Load model
        self.my_model = attempt_load(self.weights, map_location=device)  # load FP32 model
        return self.my_model

    @torch.no_grad()
    def detect(self,opt,my_model):
        source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
        save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
        webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
            ('rtsp://', 'rtmp://', 'http://', 'https://'))
        label = 'debug'  #
        # Directories
        save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

        # Initialize
        set_logging()
        device = select_device(opt.device)
        half = opt.half and device.type != 'cpu'  # half precision only supported on CUDA

        # model = attempt_load(weights, map_location=device)  # load FP32 model
        model = my_model
        stride = int(model.stride.max())  # model stride
        imgsz = check_img_size(imgsz, s=stride)  # check img_size
        names = model.module.names if hasattr(model, 'module') else model.names  # get class names
        if half:
            model.half()  # to FP16

        # Second-stage classifier
        classify = False
        if classify:
            modelc = load_classifier(name='resnet101', n=2)  # initialize
            modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

        # Set Dataloader
        vid_path, vid_writer = None, None
        if webcam:
            view_img = check_imshow()
            cudnn.benchmark = True  # set True to speed up constant image size inference
            dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        else:
            dataset = LoadImages(source, img_size=imgsz, stride=stride)

        # Run inference
        if device.type != 'cpu':
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
        t0 = time.time()
        for path, img, im0s, vid_cap in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)

            # Inference
            # t1 = time_synchronized()
            pred = model(img, augment=opt.augment)[0]

            # Apply NMS
            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms,
                                       max_det=opt.max_det)
            # t2 = time_synchronized()

            # Apply Classifier
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)

            # Process detections
            labels=[]
            cutted_res = []
            for i, det in enumerate(pred):  # detections per image
                if webcam:  # batch_size >= 1
                    p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # img.jpg
                txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                imc = im0.copy()  # for opt.save_crop
                annotator = Annotator(im0, line_width=opt.line_thickness, example=str(names))
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Write results
                    num=0
                    for *xyxy, conf, cls in reversed(det):
                        if(conf<self.save_conf):
                            return img,[],[]
                        num+=1
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')

                        if save_img or opt.save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if opt.hide_labels else (
                                names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
                            labels.append(label)
                            annotator.box_label(xyxy, label, color=colors(c, True))
                            crop_path = save_dir / 'crops' / f'{p.stem}.jpg'
                            save_one_box(xyxy, imc, file=crop_path, BGR=True, save=True)
                            print(os.path.join(save_dir, 'crops', f'{p.stem}.jpg'))
                            if num!=1:
                                crop = QPixmap(os.path.join(save_dir, 'crops', f'{p.stem}{str(num)}.jpg'))
                            else:
                                crop = QPixmap(os.path.join(save_dir, 'crops', f'{p.stem}.jpg'))
                            cutted_res.append(crop)
                #
                # # Print time (inference + NMS)
                # print(f'{s}Done. ({t2 - t1:.3f}s)')

                # Stream results
                im0 = annotator.result()
                # if view_img:
                #     cv2.imshow(str(p), im0)
                #     cv2.waitKey(1)  # 1 millisecond

                # Save results (image with detections)
                save_img=True
                if save_img:
                    if dataset.mode == 'image':
                        cv2.imwrite(save_path, im0)
                        res_im0=QPixmap(save_path)
                    else:  # 'video' or 'stream'
                        if vid_path != save_path:  # new video
                            vid_path = save_path
                            if isinstance(vid_writer, cv2.VideoWriter):
                                vid_writer.release()  # release previous video writer
                            if vid_cap:  # video
                                fps = vid_cap.get(cv2.CAP_PROP_FPS)
                                w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            else:  # stream
                                fps, w, h = 30, im0.shape[1], im0.shape[0]
                                save_path += '.mp4'
                            vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer.write(im0)

        # if save_txt or save_img:
        #     s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        #     print(f"Results saved to {save_dir}{s}")
        print(f'Done. ({time.time() - t0:.3f}s)')
        self.detected_flag=True
        return res_im0,labels,cutted_res

    def main_detect(self,source):
        parser = argparse.ArgumentParser()
        parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
        parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
        parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
        parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
        parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
        parser.add_argument('--max-det', type=int, default=1000, help='maximum number of detections per image')
        parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
        parser.add_argument('--view-img', action='store_true', help='display results')
        parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
        parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
        parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
        parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
        parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
        parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
        parser.add_argument('--augment', action='store_true', help='augmented inference')
        parser.add_argument('--update', action='store_true', help='update all models')
        parser.add_argument('--project', default='runs/detect', help='save results to project/name')
        parser.add_argument('--name', default='exp', help='save results to project/name')
        parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
        parser.add_argument('--line_thickness', default=3, type=int, help='bounding box thickness (pixels)')
        parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
        parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
        parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
        opt = parser.parse_args()
        opt.weights=self.weights
        opt.source=source
        opt.project=self.project
        opt.device=self.device
        opt.imgsz=self.imgsz
        opt.name=self.save_dir
        opt.save_txt=self.save_txt
        opt.save_conf=self.save_conf
        res_im0, labels,cutted_pics= self.detect(opt,self.my_model)
        self.res_im0 =res_im0
        self.labels =labels
        self.crops=cutted_pics
        found=False if len(labels)==0 else True
        self.detect_finish_sign.emit(found)
        return res_im0, labels ,cutted_pics

# if __name__ == "__main__":
